Integration search strategies in tree seed algorithm for high dimensional function optimization

A multi-strategy improved tree-seed algorithm for numerical optimization and engineering optimization problems

Tree-seed algorithm is a stochastic search algorithm with superior performance suitable for solving continuous optimization problems. However, it is also prone to fall into local optimum and slow in convergence. Therefore, this paper proposes an improved tree-seed algorithm based on pattern search, dimension permutation, and elimination update mechanism (PDSTSA). Firstly, a global optimization strategy based on pattern search is used to promote detection ability. Secondly, in order to maintain the diversity of the population, a random mutation strategy of individual dimension replacement is introduced. Finally, the elimination and update mechanism based on inferior trees is introduced in the middle and later stages of the iteration. Subsequently, PDSTSA is compared with seven representative algorithms on the IEEE CEC2015 test function for simulation experiments and convergence curve analysis. The experimental results indicate that PDSTSA has better optimization accuracy and convergence speed than other comparison algorithms. Then, the Wilcoxon rank sum test demonstrates that there is a significant difference between the optimization results of PDSTSA and each comparison algorithm. In addition, the results of eight algorithms for solving engineering constrained optimization problems further prove the feasibility, practicability, and superiority of PDSTSA.

Advances in Sparrow Search Algorithm: A Comprehensive Survey

Mathematical programming and meta-heuristics are two types of optimization methods. Meta-heuristic algorithms can identify optimal/near-optimal solutions by mimicking natural behaviours or occurrences and provide benefits such as simplicity of execution, a few parameters, avoidance of local optimization, and flexibility. Many meta-heuristic algorithms have been introduced to solve optimization issues, each of which has advantages and disadvantages. Studies and research on presented meta-heuristic algorithms in prestigious journals showed they had good performance in solving hybrid, improved and mutated problems. This paper reviews the sparrow search algorithm (SSA), one of the new and robust algorithms for solving optimization problems. This paper covers all the SSA literature on variants, improvement, hybridization, and optimization. According to studies, the use of SSA in the mentioned areas has been equal to 32%, 36%, 4%, and 28%, respectively. The highest percentage belongs to Improved, which has been analyzed by three subsections: Meat-Heuristics, artificial neural networks, and Deep Learning.

Fractal adaptive weight synthesized-local directional pattern-based image classification using enhanced tree seed algorithm

Coral reefs are one of the most prominent marine ecosystems on the Earth; they are threatened due to various factors, including growing anthropogenic impacts and the effects of global change. Hence, the automatic classification of coral species is significant for tracking and detecting threatened and susceptible coral species. This paper proposes a new feature descriptor known as fractal adaptive weight synthesized-local directional pattern (FAWS-LDP) method to classify coral images using enhanced tree seed algorithm with extreme learning machine (ETSA-ELM) technique. The proposed feature descriptor (FAWS-LDP) inherits the advantage of both fractal pixel intensity information and local directional characteristics by indexing both feature vector values. Finally, the extracted features are imported to the extreme learning machine (ELM) network for classification. The ELM classifier is a single hidden layer feed-forward neural network with a faster learning speed and produces good generalization performance. The random selection of input weight and biases of the ELM classifier produces non-optimal or unnecessary input biases and weights to the network. Hence, to fine-tune the parameters of the ELM classifier, an enhanced tree seed algorithm (ETSA) is proposed. The proposed ETSA is a new learning technique to overcome the drawbacks like local optima and a lower coverage rate. The classification performance of ELM employing the ETSA optimizer is compared to the original TSA and other well-known metaheuristic algorithm (MHA) trainers, such as genetic algorithm (GA), particle swarm optimization (PSO), and artificial bee colony (ABC), utilizing model performance measures like specificity, sensitivity, and classification accuracy. The evaluation of coral classification datasets reveals that the proposed ETSA-ELM produces consistently superior performance to existing methods. Finally, the proposed feature descriptor technique is statistically analyzed using a non-parametric Friedman test to demonstrate the efficiency.